As Digital TV subscribers are offered more and more channels, it is becoming
increasingly difficult for them to locate the right programme information at
the right time. The personalized Electronic Programme Guide (pEPG) is one
solution to this problem; it leverages artificial intelligence and user
profiling techniques to learn about the viewing preferences of individual users
in order to compile personalized viewing guides that fit their individual
preferences. Very often the limited availability of profiling information is a
key limiting factor in such personalized recommender systems. For example, it
is well known that collaborative filtering approaches suffer significantly from
the sparsity problem, which exists because the expected item-overlap between
profiles is usually very low. In this article we address the sparsity problem
in the Digital TV domain. We propose the use of data mining techniques as a way
of supplementing meagre ratings-based profile knowledge with additional
item-similarity knowledge that can be automatically discovered by mining user
profiles. We argue that this new similarity knowledge can significantly enhance
the performance of a recommender system in even the sparsest of profile spaces.
Moreover, we provide an extensive evaluation of our approach using two
large-scale, state-of-the-art online systems -- PTVPlus, a personalized TV
listings portal and Físchlár, an online digital video library
system.

Watching television tends to be a social activity. So, adaptive television
needs to adapt to groups of users rather than to individual users. In this
paper, we discuss different strategies for combining individual user models to
adapt to groups, some of which are inspired by Social Choice Theory. In a first
experiment, we explore how humans select a sequence of items for a group to
watch, based on data about the individuals' preferences. The results show that
humans use some of the strategies such as the Average Strategy (a.k.a. Additive
Utilitarian), the Average Without Misery Strategy and the Least Misery
Strategy, and care about fairness and avoiding individual misery. In a second
experiment, we investigate how satisfied people believe they would be with
sequences chosen by different strategies, and how their satisfaction
corresponds with that predicted by a number of satisfaction functions. The
results show that subjects use normalization, deduct misery, and use the
ratings in a non-linear way. One of the satisfaction functions produced
reasonable, though not completely correct predictions. According to our
subjects, the sequences produced by five strategies give satisfaction to all
individuals in the group. The results also show that subjects put more emphasis
than expected on showing the best rated item to each individual (at a cost of
misery for another individual), and that the ratings of the first and last
items in the sequence are especially important. In a final experiment, we
explore the influence viewing an item can have on the ratings of other items.
This is important for deciding the order in which to present items. The results
show an effect of both mood and topical relatedness.

Although programpreferences can be characterized on the basis of demographic
attributes like sex, age or occupation or by taking the cultural studies
approach focused on ethnic or social traits, preferences for programs often
differ among people of the same sex, age, occupation and social class. We think
that nothing can describe subjects' viewing preferences more accurately than
what programs they had watched in the past. To verify our hypothesis, we
surveyed the viewing behavior of more than 1,600 randomly chosen individuals,
and utilized this data to analyze people's program choices. We categorized the
respondents by the similarity of the programs they had watched and examined the
groupings that emerged and the features of these groups.
From our analysis, it became clear that a 'more/less serious' and 'more/less
fictional' axes are involved in program selection.
Our results show that eight groups (stereotypes) explain viewers' contact
with television, their motivation for choosing programs to watch, and their
interest in matters other than television. Applying these stereotypes to the
process of program selection or recommendation will be useful for the future
design of personalized adaptive systems.

Broadcast news sources and newspapers provide society with the vast majority
of real-time information. Unfortunately, cost efficiencies and real-time
pressures demand that producers, editors, and writers select and organize
content for stereotypical audiences. In this article we illustrate how content
understanding, user modeling, and tailored presentation generation promise
personalcasts on demand. Specifically, we report on the design and
implementation of a personalized version of a broadcast news understanding
system, MITRE's Broadcast News Navigator (BNN), that tracks and infers user
content interests and media preferences. We report on the incorporation of
Local Context Analysis to both expand the user's original query to the most
related terms in the corpus, as well as to allow the user to provide
interactive feedback to enhance the relevance of selected newsstories. We
describe an empirical study of the search for stories on ten topics from a
video corpus. By personalizing both the selection of stories and the form in
which they are delivered, we provide users with tailored broadcast news. This
individual news personalization provides more fine-grained content tailoring
than current personalized television program level recommenders and does not
rely on externally provided program metadata.

A case study in adaptive information filtering systems for the Web is
presented. The described system comprises two main modules, named HUMOS and
WIFS. HUMOS is a user modeling system based on stereotypes. It builds and
maintains long term models of individual Internet users, representing their
information needs. The user model is structured as a frame containing
informative words, enhanced with semantic networks. The proposed machine
learning approach for the user modeling process is based on the use of an
artificial neural network for stereotype assignments. WIFS is a content-based
information filtering module, capable of selecting html/text documents on
computer science collected from the Web according to the interests of the user.
It has been created for the very purpose of the structure of the user model
utilized by HUMOS. Currently, this system acts as an adaptive interface to the
Web search engine ALTA VISTATM. An empirical evaluation of the system has been
made in experimental settings. The experiments focused on the evaluation, by
means of a non-parametric statistics approach, of the added value in terms of
system performance given by the user modeling component; it also focused on the
evaluation of the usability and user acceptance of the system. The results of
the experiments are satisfactory and support the choice of a user model-based
approach to information filtering on the Web.

The aim in information filtering is to provide users with a personalised
selection of information, based on their interest profile. In adaptive
information filtering, this profile partially or completely acquired by
automatic means. This paper investigates if profile generation can be partially
acquired by automatic methods and partially by direct user involvement. The
issue is explored through an empirical study of a simulated filtering system
that mixes automatic and manual profile generation. The study covers several
issues involved in mixed control. The first issue concerns if a machine-learned
profile can provide better filtering performance if generated from an initial
explicit user profile. The second issue concerns if user involvement can
improve on a system-generated or adapted profile. Finally, the relationship
between filtering performance and user ratings is investigated. In this
particular study the initial setup of a personal profile was effective and
yielded performance improvements that persisted after substantiate training.
However, the study showed no correlation between users' ratings of profiles and
profile filtering performance, and only weak indications that users could
improve profiles that already had been trained on feedback.

SiteIF is a personal agent for a bilingual news web site that learns user's
interests from the requested pages. In this paper we propose to use a word
sense based document representation as a starting point to build a model of the
user's interests. Documents passed over are processed and relevant senses
(disambiguated over WordNet) are extracted and then combined to form a semantic
network. A filtering procedure dynamically predicts new documents on the basis
of the semantic network.
There are two main advantages of a sense-based approach: first, the model
predictions, being based on senses rather than words, are more accurate;
second, the model is language independent, allowing navigation in multilingual
sites. We report the results of a comparative experiment that has been carried
out to give a quantitative estimation of these improvements.

A web-based search engine responds to a user's query with a list of
documents. This list can be viewed as the engine's model of the user's idea of
relevance -- the engine 'believes' that the first document is the most likely
to be relevant, the second is slightly less likely, and so on. We extend this
idea to an interactive setting where the system accepts the user's feedback and
adjusts its relevance model. We develop three specific models that are
integrated as part of a system we call Lighthouse. The models incorporate
document clustering and a spring-embedding visualization of inter-document
similarity. We show that if a searcher were to use Lighthouse in ways
consistent with the model, the expected effectiveness improves -- i.e., the
relevant documents are found more quickly in comparison to existing methods.

UMUAI 2004 Volume 14 Issue 4

Initializing a student model for individualized tutoring in educational
applications is a difficult task, since very little is known about a new
student. On the other hand, fast and efficient initialization of the student
model is necessary. Otherwise the tutoring system may lose its credibility in
the first interactions with the student. In this paper we describe a framework
for the initialization of student models in Web-based educational applications.
The framework is called ISM. The basic idea of ISM is to set initial values for
all aspects of student models using an innovative combination of stereotypes
and the distance weighted k-nearest neighbor algorithm. In particular, a
student is first assigned to a stereotype category concerning her/his knowledge
level of the domain being taught. Then, the model of the new student is
initialized by applying the distance weighted k-nearest neighbor algorithm
among the students that belong to the same stereotype category with the new
student. ISM has been applied in a language learning system, which has been
used as a test-bed. The quality of the student models created using ISM has
been evaluated in an experiment involving classroom students and their
teachers. The results from this experiment showed that the initialization of
student models was improved using the ISM framework.

The work described here pertains to ICICLE, an intelligent tutoring system
for which we have designed a user model to supply data for intelligent natural
language parse disambiguation. This model attempts to capture the user's
mastery of various grammatical units and thus can be used to predict the
grammar rules he or she is most likely using when producing language. Because
ICICLE's user modeling component must infer the user's language mastery on the
basis of limited writing samples, it makes use of an inferencing mechanism that
will require knowledge of stereotypic acquisition sequences in the user
population. We discuss in this paper the methodology of how we have applied an
empirical investigation into user performance in order to derive the sequence
of stereotypes that forms the basis of our modeling component's reasoning
capabilities.

This research aims to support collaborative distance learners by
demonstrating how a probabilistic machine learning method can be used to model
and analyze online knowledge sharing interactions. The approach applies Hidden
Markov Models and Multidimensional Scaling to analyze and assess sequences of
coded online student interaction. These analysis techniques were used to train
a system to dynamically recognize (1) when students are having trouble learning
the new concepts they share with each other, and (2) why they are having
trouble. The results of this research may assist an instructor or intelligent
coach in understanding and mediating situations in which groups of students
collaborate to share their knowledge.

UMUAI 2004 Volume 14 Issue 5

Search engines continue to struggle with the challenges presented by Web
search: vague queries, impatient users and an enormous and rapidly expanding
collection of unmoderated, heterogeneous documents all make for an extremely
hostile search environment. In this paper we argue that conventional approaches
to Web search -- those that adopt a traditional, document-centric, information
retrieval perspective -- are limited by their refusal to consider the past
search behaviour of users during future search sessions. In particular, we
argue that in many circumstances the search behaviour of users is repetitive
and regular; the same sort of queries tend to recur and the same type of
results are often selected. We describe how this observation can lead to a
novel approach to a more adaptive form of search, one that leverages past
search behaviours as a means to re-rank future search results in a way that
recognises the implicit preferences of communities of searchers. We describe
and evaluate the I-SPY search engine, which implements this approach to
collaborative, community-based search. We show that it offers potential
improvements in search performance, especially in certain situations where
communities of searchers share similar information needs and use similar
queries to express these needs. We also show that I-SPY benefits from important
advantages when it comes to user privacy. In short, we argue that I-SPY strikes
a useful balance between search personalization and user privacy, by offering a
unique form of anonymous personalization, and in doing so may very well provide
privacy-conscious Web users with an acceptable approach to personalized search.

We introduce a methodology to improve Adaptive Systems for Web-Based
Education. This methodology uses evolutionary algorithms as a data mining
method for discovering interesting relationships in students' usage data. Such
knowledge may be very useful for teachers and course authors to select the most
appropriate modifications to improve the effectiveness of the course. We use
Grammar-Based Genetic Programming (GBGP) with multi-objective optimization
techniques to discover prediction rules. We present a specific data mining tool
that can help non-experts in data mining carry out the complete rule discovery
process, and demonstrate its utility by applying it to an adaptive Linux course
that we developed.